The lack of exploitation of prior upstanding works related to video action recognition, video motions, trajectories analysis, object, and classification is poor.
We saw that best works take advantage of many motion features related to time or temporal cues.
Another essential aspect that could be improved is the evaluation metrics used to report performance (and therefore rank the models), because the most used are those not related to semantic aspect; we are aware of new metrics such as BLEURT [1]}, BERTScore [2]}, BERTr [3]}, CLIPScore [4]} which use semantic or contextual embeddings to evaluate the semantic relationship of words.
These approaches take advantage of considering the whole sentence for their embeddings (e.g., BERTScore's bidirectionality) being able to distinguish between polysemous words and homonyms, due to the context of the description (or references in the training phase).
